Verbinden Sie Neptuneai ML Experiment Tracking mit Ihrer KI

• 01 / 03Modellversionen prüfen
• 02 / 03ML-Metriken überwachen
• 03 / 03Metadaten abfragen

Schnellster Tag

757ms

Langsamster Tag

1087ms

14-Tage-Trend

Stabil-2%

Neptune.ai Connector

Alles bereit. Wählen Sie Ihren MCP-Client und folgen Sie den Einrichtungsschritten.

Connector-Linkhttps://edge.vinkius.com/vk_preview_HI0D1zHVFTWAvCQH47Rd6lplgg0GBJXVQZ0fMTHm/mcpKopieren

Claude Desktop

Folgen Sie den Schritten unten, um sich in Sekunden zu verbinden.

1. 1In Claude Desktop, open Settings → Connectors.
2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3. 3Click Add and start a new chat — Neptune.ai capabilities are ready to use.

Konfiguration · claude_desktop_config.jsonKopieren

{
  "mcpServers": {
    "neptuneai-ml-experiment-tracking-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_HI0D1zHVFTWAvCQH47Rd6lplgg0GBJXVQZ0fMTHm/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

Schritt-für-Schritt-Anleitungen für jeden Client finden Sie im Guide. Verbinden

Geführte Einrichtung für Claude? Neptune.ai in Claude und anderen KI-Tools einrichten

Alle KI-Clients ansehen, mit denen dieser Connector funktioniert ↑

• 01

  Data Scientists

  Überwachen den Trainingsfortschritt und prüfen Modellmetriken per Chat, ohne Dashboards nutzen zu müssen.

• 02

  ML Engineers

  Auditieren das Model Registry und verifizieren Experiment-Attribute direkt über das Terminal ihres Arbeitsbereichs.

• 03

  AI Researchers

  Verfolgen Produktionsmodelle und stellen eine konsistente Metadaten-Protokollierung über mehrere Projekte sicher.

• 01

  How do I connect my Neptune.ai data to my AI client?

  You subscribe to the MCP on Vinkius and provide your Neptune.ai API Token. Once connected, you can use it immediately in Claude, Cursor, or Windsurf.

• 02

  Can I see my training loss curves through the AI?

  Yes, the MCP allows your agent to extract telemetry like accuracy metrics and loss curves from specific execution checkpoints.

• 03

  Does this work with my existing Neptune.ai projects?

  Yes, the MCP uses your API token to access the projects and workspaces already managed in your Neptune.ai account.

• 04

  What kind of models can I manage?

  You can list and retrieve trained models that have been promoted or logged within your Neptune.ai project registry.

• 05

  Is my API token secure?

  Vinkius manages your credentials and keeps them secure so you can connect your client once and stay connected.

• 06

  Can I see the accuracy metrics for a specific ML run through my agent?

  Yes. Use the get_attributes capability with your Project ID and Run ID. Your agent will retrieve the detailed telemetry logged during that execution, including accuracy, loss, and any custom attributes defined in your code.

• 07

  How do I check which model versions are currently stable in my registry?

  The list_models capability retrieves all packaged ML models within a project. Your agent will expose the promoted model versions, helping you distinguish between experimental runs and stable candidates ready for deployment.

• 08

  Can my agent search through hundreds of past ML experimentation runs?

  Absolutely. Use the search_runs capability with your Project ID. Your agent will query Neptune's tracking server to identify historical experiment state checkpoints, making it easy to locate specific training results across your entire research timeline.

• 

  Comet ML KI-Connector

  Manage machine learning experiments via Comet — track model metrics, audit project workspaces, and inspect ML

  Ansehen
• 

  Weights & Biases KI-Connector

  Track experiments, monitor ML runs, and manage artifacts on WandB — the developer platform for AI.

  Ansehen
• 

  Hevo Data (ETL & Data Pipeline) KI-Connector

  Manage data pipelines via Hevo — list pipelines, monitor destinations, and track usage.

  Ansehen
• 

  Dataiku DSS KI-Connector

  Manage data science via Dataiku — list projects and datasets, track pipeline jobs, run automation scenarios, a

  Ansehen

Vorschläge

• 

  Data Pipeline Prover KI-Connector

  A data team asked an AI to build an ETL pipeline. No schema contract. No idempotency. No freshness SLA. The pi

  Ansehen
• 

  ML Experiment Tracking Cost Analyzer KI-Connector

  Calculate infrastructure, storage, and knowledge management costs for ML experiments.

  Ansehen
• 

  DataRobot KI-Connector

  Manage AutoML via DataRobot — monitor projects and models, track deployments, and audit ML datasets directly f

  Ansehen
• 

  MLflow (ML Lifecycle Management) KI-Connector

  Manage ML lifecycle via MLflow — track training runs, monitor metrics, and audit the model registry.

  Ansehen